{Reference Type}: Journal Article
{Title}: Machine learning approaches to predict whether MEPs can be elicited via TMS.
{Author}: Jin F;Bruijn SM;Daffertshofer A;
{Journal}: J Neurosci Methods
{Volume}: 410
{Issue}: 0
{Year}: 2024 Oct 9
{Factor}: 2.987
{DOI}: 10.1016/j.jneumeth.2024.110242
{Abstract}: <B>BACKGROUND: </B>Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, which after transmission along cortico-muscular pathways can be measured as motor-evoked potentials (MEPs). The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs.<BR><B>METHODS: </B>We sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs.<BR><B>RESULTS: </B>We obtained prediction accuracies of on average 77 % and 65 % with maxima up to up to 90 % and 72 % within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs.<BR><B>CONCLUSIONS: </B>Although within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.